4 citations · 4 across the 3 of their papers we have counts for
3 papers
stat.ML2026
A convolutional framework for detecting event-driven dynamics in energy price series
Caixia Xu, Piotr Fryzlewicz
This paper develops a general convolutional neural network (CNN) framework for detecting heterogeneous event-driven dynamics in univariate time series windows. We show that the ind…
stat.ME2026
High-dimensional sparsity-adaptive multiple change-point detection
Hyeyoung Maeng, Tengyao Wang, Piotr Fryzlewicz
We introduce a method for detecting multiple change-points in the mean of a high-dimensional data sequence. Unlike existing top-down (i.e. divisive) algorithms, we adopt a bottom-u…
stat.ME2024★ 4 cited
Multiscale Autoregression on Adaptively Detected Timescales
Rafal Baranowski, Yining Chen, Piotr Fryzlewicz
We propose a multiscale approach to time series autoregression, in which linear regressors for the process in question include features of its own path that live on multiple timesc…